A digital image of an object is formed as an array of rows and columns of pixels, which correspond to pixels on a target pixel display such as a conventional display screen or other display device. Pixels are typically identified by their x,y coordinates in the pixel array. Each pixel has one or more associated pixel values that represent an incremental area of the object. When the image is a black and white image, the pixel value represents a gray shade of the incremental area. When the image is a color image, red, green and blue (R,G,B) pixel values are associated with each pixel. Examples of digital images include, but are not limited to, medical images such as digital x-rays and MRI images, digital film images, digital video images and satellite images.
Spatial transformations may be performed on a digital image. These kinds of transformations include scaling, rotation, translation, or other types of affine transforms. These transformation are then applied to an image or a portion of an image. When an image of an object is magnified then resampling is required to map the source image to a magnified target image since a magnified target image is represented by a greater number of pixels than the source image. Conventional transformation processing software has been developed to provide for transformation of image objects for image display. However, typically a large amount of processing resources are required to perform high-quality resampling which is conducted on a pixel-by-pixel basis. Faster resampling methods suffer from lower quality results. For example, pixel replication methods generate blocky sampling-position artifacts.